Lorenzo Gaifas
I joined the napari community during my PhD in structural biology, where I used and contributed to napari regularly, until I was invited to join the core team. I now work full time on napari as an independent contractor, improving and developing many of the features that I used or introduced during my PhD.
he/him
napari
Independent Contractor
Session
If you work with scientific data, chances are that visualization is one of your strongest tools and biggest time sinks. Whether you're dealing with images from microscopes or telescopes, complex surface reconstructions, 3D point clouds, or n-dimensional feature embeddings from neural networks, some requirements are always the same: performance, interactivity, and extensibility.
napari is a Python library for the visualization and annotation of scientific data that focuses on addressing these needs, staying cross-field and un-specialized at the core, while providing an easy way to develop powerful specialized plugins.
In this tutorial, we will learn the basics of interacting with napari and its features and how to use napari to effectively navigate n-dimensional data. Armed with this knowledge, we will simulate a typical exploratory approach to developing a new image processing workflow in Python and converting it to an easily shearable napari plugin.